De novo design of protein structure and function with RFdiffusion

Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek and David Baker ()
Additional contact information
Joseph L. Watson: University of Washington
David Juergens: University of Washington
Nathaniel R. Bennett: University of Washington
Brian L. Trippe: University of Washington
Jason Yim: University of Washington
Helen E. Eisenach: University of Washington
Woody Ahern: University of Washington
Andrew J. Borst: University of Washington
Robert J. Ragotte: University of Washington
Lukas F. Milles: University of Washington
Basile I. M. Wicky: University of Washington
Nikita Hanikel: University of Washington
Samuel J. Pellock: University of Washington
Alexis Courbet: University of Washington
William Sheffler: University of Washington
Jue Wang: University of Washington
Preetham Venkatesh: University of Washington
Isaac Sappington: University of Washington
Susana Vázquez Torres: University of Washington
Anna Lauko: University of Washington
Valentin Bortoli: École Normale Supérieure rue d’Ulm
Emile Mathieu: University of Cambridge
Sergey Ovchinnikov: Harvard University
Regina Barzilay: Massachusetts Institute of Technology
Tommi S. Jaakkola: Massachusetts Institute of Technology
Frank DiMaio: University of Washington
Minkyung Baek: Seoul National University
David Baker: University of Washington

Nature, 2023, vol. 620, issue 7976, 1089-1100

Abstract: Abstract There has been considerable recent progress in designing new proteins using deep-learning methods1–9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.nature.com/articles/s41586-023-06415-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:620:y:2023:i:7976:d:10.1038_s41586-023-06415-8

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-023-06415-8

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().